{"title":"Multi-Class Cardiovascular Diseases Diagnosis from Electrocardiogram Signals using 1-D Convolution Neural Network","authors":"Mehdi Fasihi, M. Nadimi-Shahraki, A. Jannesari","doi":"10.1109/IRI49571.2020.00060","DOIUrl":null,"url":null,"abstract":"The electrocardiogram (ECG) is an important signal in the health informatics for the detection of cardiac abnormalities. There have been several researches on using machine learning techniques for analyzing ECG. However, they need additional computation owning to ECG signals challenges. We introduce a new architecture of 1-D convolution neural network (CNN) to diagnose arrhythmia diseases automatically. The proposed architecture consists of four convolution layers, three pooling layers, and three fully connected layers evaluated on the arrhythmia dataset. All previous researches are conducted to classify healthy people from people with Arrhythmia disease. In this paper, we propose to go further multiclass classification with two classes of cardiac diseases and one class of healthy people. The results are compared with common 1-D CNN and seven different classifiers. The experimental results demonstrate that the proposed architecture is superior to existing classifiers and also competitive with state of the art in terms of accuracy.","PeriodicalId":93159,"journal":{"name":"2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science : IRI 2020 : proceedings : virtual conference, 11-13 August 2020. IEEE International Conference on Information Reuse and Integration (21st : 2...","volume":"33 1","pages":"372-378"},"PeriodicalIF":0.0000,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science : IRI 2020 : proceedings : virtual conference, 11-13 August 2020. IEEE International Conference on Information Reuse and Integration (21st : 2...","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IRI49571.2020.00060","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7
Abstract
The electrocardiogram (ECG) is an important signal in the health informatics for the detection of cardiac abnormalities. There have been several researches on using machine learning techniques for analyzing ECG. However, they need additional computation owning to ECG signals challenges. We introduce a new architecture of 1-D convolution neural network (CNN) to diagnose arrhythmia diseases automatically. The proposed architecture consists of four convolution layers, three pooling layers, and three fully connected layers evaluated on the arrhythmia dataset. All previous researches are conducted to classify healthy people from people with Arrhythmia disease. In this paper, we propose to go further multiclass classification with two classes of cardiac diseases and one class of healthy people. The results are compared with common 1-D CNN and seven different classifiers. The experimental results demonstrate that the proposed architecture is superior to existing classifiers and also competitive with state of the art in terms of accuracy.